In this paper, a new approach based on grey wolf optimization (GWO) algorithm has been presented and successfully applied to solve the **combined** **economic** **emission** **dispatch** problem considering transmission losses. The problem has been formulated as multiobjective optimization problem with competing fuel cost and environmental impact objectives. The effectiveness of proposed algorithm is demonstrated on the standard IEEE 30-bus test system with six generating units. The comparison of the results obtained with other methods reported in the literature shows the superiority of the proposed algorithm and its potential for solving the **combined** **economic** **emission** **dispatch** problems in large- scale power systems. The results obtained from the test systems have indicated that the proposed technique has better performance in terms of minimum fuel costs and NO x emissions than other optimization methods reported

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This paper has presented a new optimization algorithm to solve the **combined** **economic** **emission** **dispatch** problem considering linear equality and inequality constraints and also considering transmission losses. **Economic** and **emission** **dispatch** is a multi-objective problem. But the present approach makes use of only one objective function and depending upon the problem such as **economic**, **emission** or **combined** **economic** and **emission** **dispatch**, only the coefficients of the objective function has to be changed. The feasibility of the proposed method for solving CEED problems is demonstrated using IEEE 30-bus test system with six generating units. The comparison of the results with other methods reported in the literature shows the superiority of the proposed method and its potential for solving CEED problems in a power system. From the results obtained, it can be concluded that the MABC algorithm is a promising technique for solving complex optimization problems in power system operation.

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This paper deals with particle swarm optimization (PSO) method to solve **Combined** **Economic** **emission** **Dispatch** Problem (CEEDP)of thermal units while satisfying the constraints such as generator capacity limits, power balance and line flow limits. PSO is a stochastic optimization process based on the movement and intelligence of swarms. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units. The bi-objective problem is converted into single objective problem by introducing price penalty factor to maintain an acceptable system performance in terms of limits on generator real power outputs, transmission losses with minimum **emission** **dispatch**. The proposed approach has been evaluated on an IEEE 30-bus test system with six generators. The results obtained with the proposed approach are compared with results of genetic algorithm and other technique.

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Abstract:- For large power system operation **Economic** Load **Dispatch** is one of the most important problem. Its objective how to schedule generation at various inter connected generating plants , to meet required load demand, considering system constraints to keep operating cost at minimum level. In **combined** **economic** **emission** **dispatch** (ceed) not only minimize the operating but simultaneously keep **emission** level low also. There are various technique, proposed by several researchers to solve ceed problem. In this paper Jaya, Particle Swarm Optimization and Bare-Born Particle Swarm Optimization and Differential Evolutionary algorithms are applied to minimize operating cost with minimization of **emission** too. Generation for various units and Power loss is calculated using Newton-Raphson power flow method on IEEE-6 and IEEE-14 bus test data.

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Abstract— In electrical systems, the objective of CEED issue is to search for an optimum schedule for the entire generators to reduce the functioning fuel cost when gratifying all types of parameters, namely, generation capacity factors and load demand balance factor. Anyhow, with the growing public awareness on the environmental issue occurred by fossil fuels, it is vital not only to be concerned for **economic** gain but also to deal with the **emission** issue of fossil fuels. As a result, the objective of **emission** must also be concerned. Accordingly, this survey intends to review various topics to solve CEED issues in system network. Accordingly, the performance measures and the maximum performance achievements are also analyzed and demonstrated in this survey. In addition, the algorithmic classification for the surveyed papers is analyzed and described. Finally, the research issues of the suggested model are also discussed briefly.

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The planning and operation of a modern power system in an optimal way involves the integration of variable renewable energy sources, system security, the consideration of economy of operation, emissions at fossil-fuel plants, optimal release of water at hydro power plants, power purchase agreement etc. all in the problem formulation for a practical power system. Several optimization algorithms have been used to analyze the **economic** and environmental **dispatch** problem. Conventional methods such as dynamic programming [10], Lagrange's technique [20], and lambda-iteration [3], [16] have been reported in the literature to find the global optimal solution for **economic** and environmental **dispatch** problem. However, these techniques usually suffer from slow convergence rate, large computation time, poor local optima avoidance, and algorithm complexity. More recently, improved heuristic and meta- heuristic techniques like Artificial Bee Colony Method [5], [9], Binary Particle Swarm Optimization technique [7], Egyptian Vulture Optimization Algorithm [13], Spiral Optimization Algorithm [14], Genetic algorithm [15], Simulated Annealing

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presented techniques. The modified price penalty factor for the 10500 MW load demand is 0.88011 ($/Ton) giving the exact best total CEED cost as 327634.7954 ($/h). The convergence characteristic is shown in Figure-8 and the cost comparisons are illustrated in Figure-9. From the results it is seen that the WWOA provides a high quality solution better than those obtained by MABC and BSA with fast convergence and robustness for solving complex large **combined** **economic** **emission** **dispatch** problems. Table-7. Comparison of generator values for forty-unit system including v. p. effect (Pd =10500 MW).

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Abstract—Power system planners are forced to consider the alarming rate of environmental pollution and rapiddepletion of fossil fuels andutilize renewable energy resources to mitigate the environmental effects of thermal power stations. **Combined** **Economic** **Emission** **Dispatch**(CEED)offers an effectivesolution to reducefossil fuel emissions as well ascost.Since 1985, CEED is considered to be a common optimization strategy. Literature contains lot of optimization methods for the strategy.In the recent times, using PV energy has proved to be a feasible and dependable alternative for electricity generation systems based on fossil fuels. In the developing countries, the dependency on fossil fuels has been seen as inevitable. At present,the use of renewable energy sources is rapidly increasing in inconventional power generation systems.

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rate limit and prohibited zones constraints of all units are considered to check the adequacy of the PSO-TLBO algorithm for **combined** **economic** **emission** **dispatch**. The data are used from [21] for cost coefficients, active power limits, ramp rate limits, and prohibited zones. Table 8, 9 provides information of cost and **emission** coefficients. Table 10 provides the prohibited zones and ramp rate limit and table 11 provides loss coefficients.

[11] C.N. Ravi1, Dr. C. Christober Asir Rajan2, “Differential Evolution technique to solve **Combined** **Economic** **Emission** **Dispatch**”, 3rd International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA'2013) January 26-27, 2013 Hong Kong (China). [12] Naveen Kumar, K. P. Singh Parmar, “Optimal Solution of **Combined** **Economic** **Emission** Load **Dispatch** using Genetic Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 48– No.15, June 2012.

The **economic** **dispatch** and **emission** **dispatch** are two distinct problems. **Emission** **dispatch** can be included in conventional **economic** load **dispatch** problems by the adding of **emission** cost to the normal **dispatch** cost. Actually, CEED problem have two objectives. But CEED can be converted into single objective optimization problem by introducing a price penalty factor h (Rs/lb) as follows:

conomic **Dispatch** (ED) optimization is the most important issue which is to be taken into consideration in power systems. The problem of ED in power systems is to plan the power output for each devoted generator unit in such a way that the operating cost is minimized and simultaneously, matching load demand, power operating limits and maintaining stability. The total generator operating cost includes fuel, labor, supplies and maintenance costs. For simplicity we consider fuel cost as the only variable cost since generally the costs of labor, supplies and maintenance are fixed percentages of the fuel cost. Hence only thermal plants are considered in this research. Over the recent years there has been much research in the area of the **combined** **economic** and **emission** **dispatch** problem. Gopala Krishnan et al, 2011 [1] outlines a summary of techniques that have been applied so far to the **combined** **economic** and **emission** **dispatch** problem. The paper highlights new techniques which have been applied to the CEED problem from 2000-2010. It also highlights challenges faced by the use of traditional methods due to the non linearity of cost functions. It generally encourages the use of PSO. Biswajit Purkayasha et al, 2010 [2] aims at non dominated solutions in considering the multi-objective optimization problem of **economic** and **emission** **dispatch** using Non-dominated Sorting GA II. The result demonstrates it’s effectiveness in solving the multi-objective problem. It considers the cost of fuel, SOx and NOx. Celal Yasar et al, 2005 [3] uses the first order gradient method in solving the **Combined** **Economic** and **Emission** **Dispatch** problem. It has the advantage of easy control of constraints. Also all intermediate solutions are feasible for application to the power system. Anurag Gupta et al, 2012 [4] uses PSO

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The ED with piecewise quadratic cost function (EDPQ) and ED with restricted operating zones (EDPO) are the two non-convexED problems [12]. Valve point effects producing a ripple like no convex input–output heat rate curve. Complex constrained ED is forwarded by intelligent methods including Genetic Algorithm (GA), PSO [13,14], Neural Network (NN), Evolutionary Programming(EP), Tabu search etc. [15–17]. Kennedy and Eberhart introducedPSO in 1995 [18]. In this method, movement of particles is dependent on local and social components of velocity. Moreover, maximum value of velocity, Vmax, is also an important parameter. Its low value results in local exploitation while a higher value results in global international analysis. To obtain a better control over local exploitation and global research, an inertia factor x is introduced in[19]. ED with both cost and **emission** minimization becomes multiobjectiveoptimization problem and is named as **Combined** **Emission** **Economic** **Dispatch** (CEED). Using PSO, CEED has been solved by Selvakumar et al. [20]. Zhao et al. [21] solved bid based ED using Constriction Factor PSO (CFPSO) and inertia weight. In [22], a hybrid PSO, a combination of PSO and Sub sequent Quadratic Programming (SQP), is introduced in order to solve a non-convex constrainedED problem with valve point effects. In [23], CEED has been solved using a novel PSO scheme taking into account the generator limits and power balance constraints. An improved PSO has been proposed to solve ED problem of hydro-thermal co-ordination in[24]. Authors in [25]have expected scheduled an added to PSO(EPSO) for hydro-thermal scheduling problem which takes into account discrete constraints such as power balance, hydro and thermal generation limit, reservoir storage volume, initial and terminal storage limit, water balance equation and hydro discharge limit. In[26], PSO has been used to evaluate CEED problem with equality constraints handled by different manner and multi-objective optimization problem transformed into a single objective one.

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The producer cost constants, outflow or contaminants coefficients and the generation of the three unit framework are taken from [25] and power capacity available at each load bus in a power system is studied from [26].Transmission misfortune for this system is viewed as utilizing loss coefficient grid and is assumed. ELD answer for the three-unit framework is solved by utilizing PSO. The example problem are solved for **economic** **dispatch**, **emission** **dispatch** and CEED separately and the results obtained from the three methods are compared. In all of the above solution procedure, case studies are conducted with and without the ramp rate and prohibited zone. Table 1.3 to Table 1.8 summarizes all the outcomes of various load difficulties. The total fuel cost and **emission** release of PSO method with pure **economic** **dispatch**, pure **emission** **dispatch** and **combined** **economic** and **emission** **dispatch** for load of 700MW is considered and the variation is compared in Fig.1.1, Fig.1.2, Fig.1.4 and Fig.1.5. Evaluation of the cost attained by PSO in CEED for load of 700MW is shown in Fig.1.3 and Fig.1.6. Table 1.1.and 1.2 **Emission** Coefficients, Cost and Ramp rate limits and prohibited zone limits of 3- Unit Framework

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[3] T. Niknam, H.D. Mojarrad, B.B. Firouzi, A new optimization algorithm for multi-objective **economic**/**emission** **dispatch** Int. J. Electrical Power and Energy Systems, 46 (2013) 283-293. [4] D. Aydin, S. Őzyőn, C. Yaşar and T. Liao, Artificial bee colony algorithm with dynamic population size to **combined** **economic** and **emission** **dispatch** problem Int. J. Electrical Power and Energy Systems, 54 (2014) 144-153.

In order to minimize the **combined** fuel cost and **emission** function, the value of k in Eq. (6) is varying from 0.0 to 1.0 with steps of 0.1 for each simulation. The results obtained by the proposed MPSO-TVAC are presented in Table 6. Figure 4 shows the comparison of the Pareto optimal solution achieved by the MPSO- TVAC and PSO-TVAC algorithms according to diﬀerent k values. This set of solutions can be used by system operators to choose the best solution based on their preferred objective according to the k value.

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power generation to each unit such that the cost of generation is minimum.. In this work, an **Economic** **Emission** **Dispatch** model for smart microgrids (SMG) is developed that minimizes the cost of generation and **emission**. **Combined** Evolutionary and Meta-heuristic search Algorithm (CEMS) is incorporated in Micro grid management such that each of the generators communicate about their generation cost and the demand as well as the deviations to their neighboring units. The generators regulate their power output in accordance with the data thus acquired ensuring the minimal cost of production. Two optimization algorithms are used to solve both the **economic** load **dispatch** and **emission** **dispatch** (EED) problems considering transmission losses. Simulation studies are carried out in PROTEUS software.

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Associate Professor, Dept. of EEE., Sri Chandrasekharendra Saraswathi Viswa Mahavidhyalaya Kanchipuram, Tamil Nadu, India Abstract: This paper proposes a Genetic Algorithm (GA) method to solve **Emission** constrained **Economic** **dispatch** problems. The proposed method is applied to solve the **Economic** Load **Dispatch** (ELD), **Emission** **Dispatch** (ED), **Combined** **Economic** and **Emission** **Dispatch** (CEED), **Economic** Constrained **Emission** **Dispatch** (ECED) problems. For validating the proposed method it has been tested on IEEE 30 bus system with 6 generators. The results obtained with the proposed method are compared with the conventional method.

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In recent years strategically utilizing available resources and achieving electricity at bargain prices without sacrificing social benefits is very important. The environmental pollution plays a major role as it had a major threat on the human society. Hence, it became compulsory to deliver electricity at a minimum cost as well as to maintain minimum level of emissions. Lowest emissions are considered as one of the objectives in **combined** **economic** and **emission** **dispatch** problems, along with cost economy. Atmospheric pollution due to release of gases such as nitrogen oxides (NO X ), carbon

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The **economic** **dispatch** and the **emission** **dispatch** are two different objectives. The former reduces the fuel cost of the generators without considering the **emission** economy and the latter reduces the emissions without considering the cost economy. Therefore, need is there to strike a proper balance between the two objectives. This can be achieved through the concept of **combined** **economic** and **emission** **dispatch** (CEED) [5].